Business Commons^™

Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Most students lack awareness regarding the labor market outcomes for their chosen college majors. This study aims to answer what factors affect the career expectations of graduating students at Quezon City University and how these expectations align with the prevailing labor market situation. It employed descriptive, causal, and explanatory research using a sample of 108 respondents from fourth-year information technology students for the school year 2021 to 2022. Eight of the nine null hypotheses were rejected by employing multinomial logistic and linear regression. Student fixed effects and other labor market outcomes significantly predicted salary, estimated stability, and estimated skills in …

Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

All Works

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter(or X). The research models the ’Brand Polarity Score’, which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet’s influence, characterized by the engagement metrics of the tweet and the author’s follower count. We also …

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej

All Works

This study investigates green technology adoption (GTA) among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE), focusing on the influence of corporate sustainability goals (CSG) and sustainability motivation (SM). Utilizing institutional theory, the theory of planned behavior (TPB), and resource-based view (RBV), the research highlights how SMEs integrate environmental, social, governance (ESG) and economic considerations into their CSG to enhance GTA. Addressing a gap in prior research that has largely emphasized external drivers of adoption while underexploring internal organizational mechanisms, the study conceptualizes CSG as strategic intent and models SM as a second-order construct . Based on …

Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi

All Works

Predictive maintenance (PdM) is a critical enabler of intelligent asset management in Industry 4.0, yet many existing frameworks remain difficult to operationalize due to methodological fragmentation. Common limitations include sacrificing temporal realism and class granularity for computational expediency, decoupling labeling strategy design from model hyperparameter optimization, and insufficient support for reproducibility and deployment traceability; particularly in rare-failure regimes. To address these challenges, we propose a unified, end-to-end, and fully traceable PdM framework that jointly optimizes labeling and model parameters while enforcing strict temporal fidelity. The proposed pipeline co-optimizes the failure lookahead window () and LightGBM hyperparameters within a single Bayesian …

How Leaders Build Employee Trust In Artificial Intelligence: Voice Opportunities, Humility, And Trust Transfer, Jack Mcguire, David De Cremer, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Artificial intelligence is increasingly central to organizational work, yet employee trust in AI remains fragile. Although prior research has primarily explained trust in AI through technological characteristics such as transparency, reliability, and accuracy, we argue that trust in AI is also shaped by the social context in which employees encounter these systems. Drawing on affect-as-information theory and social information processing theory, we develop and test a model in which leader-provided voice opportunities reduce employees’ negative affect about AI-related work experiences, thereby enhancing perceptions of leader trustworthiness and, in turn, trust in AI. We further propose that this indirect effect depends …

Optimising Cybersecurity Application Utilisation For Digital Learning In Nigeria: An Evidence-Constrained Hybrid Agent-Based And System-Dynamics Model, Adesegun Nurudeen Osijirin, Leonard C. Anigbo, Oliver Okechukwu

Journal of Cybersecurity Education, Research and Practice

This study examines hypothetical cybersecurity-application utilisation interventions for Computer Science undergraduates in Enugu State, Nigeria. Five author-supplied datasets, each containing 364 cases, provide audited sex-specific calibration moments for a hybrid agent-based and system-dynamics model. Seven portfolios are compared over 24 months using 400 paired replications; additional experiments examine dose, effect assumptions, targeting, academic calendars, turnover and peer structure. A 1,200-point Latin-hypercube design is applied to every original scenario. Across 37,600 runs, the main-model median proportions meeting the survey utilisation criterion are 26.4%, 48.6% and 50.3% for status quo, universal combined and equity-adaptive provision. However, matching nominal dose reduces the corresponding …

From Shadow It To Shadow Ai: A Scoping Review Of How Embedded Ai And Employee Use Increase Cybersecurity And Data Privacy Risk, Jenny Ahn, Bogdan Pavlyuchenkov, Lasette Smith, Paul Wagner, Robert J. Honomichl, Ryan Straight, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

The rapid integration of artificial intelligence into enterprise software has introduced a critical governance challenge: Shadow AI, the unmanaged and often invisible adoption of AI tools and features by employees outside formal IT oversight. Unlike traditional Shadow IT, which centers on unauthorized data storage and infrastructure, Shadow AI operates at the knowledge and decision-making layer, introducing probabilistic risks including data leakage, agentic misalignment, and indirect prompt injection that existing security frameworks are ill-equipped to address. This paper presents a Systematic Literature Review (SLR) synthesizing 38 peer-reviewed and industry sources to investigate three research questions: how organizational visibility gaps into embedded …

Quantifying Attacker Payoff In Harvest Now, Decrypt Later Attacks: A Persistence-Based Framework For Trade Secret Risk, Arjun Kumar, Gene Yang, Turiya Misra, Francis Kagai

Journal of Cybersecurity Education, Research and Practice

Harvest-now, decrypt-later (HNDL) assumes that encrypted secrets stolen today will remain worth decrypting when quantum computers eventually make that possible. But what if they do not? This paper models HNDL from the attacker’s perspective by asking a different question: how much will a stolen secret still be worth when it can finally be decrypted? We develop a conditional retained-value framework combining probabilistic Q-day timing, information depreciation, and adversary-access delay. The results reveal a sharp divide. Rapidly depreciating secrets can lose much of their strategic value before quantum decryption becomes useful, while persistent secrets can retain substantial attacker payoff even under …

Data Without Borders, Sovereignty Without Guarantees: Cybersecurity Risks Of Cross-Border Data Hosting In The Global South, Evidence And Policy Lessons From Mozambique, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The digital transformation of public and private institutions across the Global South has produced a paradox: as states digitize government services, financial systems, and citizen records, an increasing share of this data is hosted on servers physically located in the United States, the European Union, and other developed economies. This article develops a taxonomy of the cybersecurity and sovereignty risks generated by this dependency, distinguishing deliberate risks, arising from extraterritorial legal instruments such as the United States CLOUD Act, foreign surveillance regimes, and a concentrated hyperscale cloud market, from non-deliberate risks, arising from undersea cable failures, hyperscaler outages, misconfiguration, and …

Specification And Internal Assessment Of A Self Reported Protective Application Use Composite Among Computer Science Undergraduates In Enugu State Nigeria, Adesegun Nurudeen Osijirin, Leonard C. Anigbo, Oliver Okechukwu

Journal of Cybersecurity Education, Research and Practice

This study specifies a Protective Application Use Composite restricted to self-reported protective application use. A cross-sectional survey provided 364 complete records from Computer Science undergraduates at four universities in Enugu State, Nigeria, from 393 distributed questionnaires. The score is the equal-weight average of five questionnaire categories: antivirus, firewall, encryption, multi-factor authentication, and virtual private network use. Reanalysis of the author-supplied item records yielded category means from 2.097 to 2.376 and internal-consistency coefficients from 0.958 to 0.971. The composite mean was 2.170, with a standard deviation of 0.469. The first component of the category-score correlation matrix explained 50.98 percent of variance. …

Markets For Unverified Capability: Why Organizations Facing Real Adversarial Scrutiny Reject The Metrics Academic Ai-Security Research Relies On, Babar Khan Akhunzada

Journal of Cybersecurity Education, Research and Practice

Governments, insurers, and defenders increasingly rely on published capability claims about AI cybersecurity agents to inform procurement, regulation, and risk assessment. Yet a striking pattern runs through the organizations with the most at stake: enterprises and technology companies deploying frontier AI agents against real production infrastructure consistently decline to publish the accuracy metrics that academic and vendor benchmarks compete to report. This paper investigates that pattern as an economic, organizational, and policy problem rather than a purely technical one. We audit eleven independently published AI cybersecurity benchmarks and deployments using a five-dimension integrity rubric and find that benchmarks relying on …

Hidden Thermal Signatures Reveal The Microclimatic Influence Of Scheduled Cave Tourism, Matt Rowberry, John Gunn, Andrew Smith

International Journal of Speleology

Tourist activity is widely recognised as a source of cave microclimate disturbance, yet visitor-induced variability is difficult to distinguish from natural environmental fluctuations. The coronavirus pandemic created a natural experiment at Poole’s Cavern, United Kingdom, in which periods of complete closure could be compared with contrasting visitor access regimes. Two years of high resolution temperature records from six monitoring points were analysed. Harmonic regression and a state space model removed deterministic diurnal cycles and lower frequency temperature variability. The remaining residual variability differed systematically among visitor access regimes and between daytime and nighttime conditions. Spectral analysis of the residuals identified …

The Digital Responsibility Index: Quantifying Perceived Multi-Layered Responsibility In Cybersecurity, Mircea Ţălu

Journal of Cybersecurity Education, Research and Practice

In the evolving digital landscape, cybersecurity responsibility extends beyond technical safeguards to include individual behavior, organizational governance, and societal impact. This study introduces the Digital Responsibility Index (DRI), a novel metric that quantifies multi-layered accountability across four domains: technical, individual, organizational, and societal. Data were collected through scenario-based Likert-scale surveys with 120 cybersecurity professionals, supplemented by semi-structured interviews with senior leaders. Results reveal that while technical and organizational responsibilities are consistently prioritized, individual and societal accountability remain under-recognized. Leadership engagement significantly enhances responsibility scores across all layers. The proposed DRI framework provides a measurable and potentially actionable basis for assessing …

Ai-Generated Phishing Attacks: Emerging Threats And Detection Strategies For Security Operations Centers, Bahzad Khalid

Journal of Cybersecurity Education, Research and Practice

Artificial intelligence (AI) is transforming cybersecurity by strengthening defensive capabilities while simultaneously enabling more sophisticated phishing attacks. AI generated phishing emails increasingly evade traditional detection methods through improved grammar, contextual relevance and realistic communication styles creating significant challenges for Security Operations Centers (SOCs). This study investigates the effectiveness of current email security systems in detecting AI generated phishing emails compared with traditional phishing emails. A mixed method experimental approach was employed using controlled Gmail and Outlook environments to evaluate the detection and classification of multiple phishing scenarios. The results demonstrate that several AI generated phishing emails successfully bypassed initial security …

A Narrative-Driven, Socratic Instructional Framework For Teaching Adversarial Cognition In Cybersecurity, Joshua K. Goossen

Journal of Cybersecurity Education, Research and Practice

The Socratic method has demonstrated, in studies across law, medicine, and nursing, that dialectic structured questioning improves expert reasoning. Cybersecurity currently teaches tools and techniques but lacks Socratic dialogic leaving graduates underprepared for novel scenarios requiring cross-domain reasoning. Because law, medicine and nursing cultivate similar reasoning as security there is principled reason to expect reasoning benefits could transfer to security students. This paper argues that the correspondence warrants investigation. Investigation requires a named, teachable reasoning target analogous to clinical reasoning and we specify that target as adversarial cognition, a framework integrating adversary features, system architecture, implementation logic, governance structure, human …

Complementary Global–Local Feature Fusion And Ensemble Refinement For Facial-Expression Recognition On Fer2013, H. M. Shahzad, Hassan A. Ahmed

Business Faculty Publications

Facial-expression recognition (FER) on FER2013 remains challenging because of low-resolution images, class imbalance, and label ambiguity. This study presents a global–local feature-fusion framework that integrates complementary representations with validation-based ensemble refinement. A frozen DINOv2 ViT-Base captures global facial semantics, while EfficientNetB3 extracts complementary local texture features. Their fused representation is used for seven-class facial-expression classification. The classification head is first trained with targeted feature-space SMOTE, and the EfficientNetB3 branch is then partially fine-tuned. Five-view test-time augmentation (TTA) is further incorporated at inference, together with an independently trained ConvNeXt-Tiny branch to provide additional architectural diversity. Ensemble weights are selected using a …

Designing For Who Actually Shows Up: A Signal Framework For Online Adult Learners In Cybersecurity And Information Technology, Chad Whistle, Mayyada Al-Hammoshi

Journal of Cybersecurity Education, Research and Practice

Online adult learners pursuing cybersecurity and information technology credentials represent one of the fastest-growing student populations in American higher education, yet the frameworks institutions use to support their success were not designed for them. This population, disproportionately drawn from the 41.9 million Americans who hold some college credit but no credential, arrives workforce-embedded, time-constrained, and skeptical of institutional systems that previously failed to serve them. Existing persistence models grounded in traditional student integration theory inadequately account for the behavioral patterns, motivational structures, and credential expectations that define this learner. This paper proposes the SIGNAL Framework (Skills-based credential architecture, Integrated AI-informed …

Bridging Theory And Practice: A Data-Driven Community Partnership To Optimize Maintenance Effectiveness, Daniel Wu

Academic Affairs

This project established an immersive, industry-engaged learning experience that embedded Data Science students in a professional business environment through a partnership with Thiele Kaolin. Students applied advanced data mining, machine learning, and big data analytics techniques to large proprietary datasets to address authentic business challenges and generate actionable, data-driven insights. By working with complex real-world data, students bridged the gap between classroom theory and industry practice while gaining hands-on experience with the full data science workflow, including data preparation, exploratory analysis, predictive modeling, and results interpretation. The project also fostered critical thinking, teamwork, and professional competencies. Students collaborated in multidisciplinary …

Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc

Journal of Global Hospitality and Tourism

This study investigates consumer responses to low-anthropomorphic service robots in restaurant front of-house roles using the AIDUA (Artificially Intelligent Device Use Acceptance). Data from 1,268  participants were analysed using PLS-SEM. The results revealed that social impact and  anthropomorphism significantly influenced both performance and effort expectancy, while hedonic  motivation influenced only performance expectancy. Performance expectancy strongly influenced  emotions, which in turn significantly influenced both the willingness to use service robots and objections  to their use. However, effort expectancy did not significantly influence emotions. The findings validate the  AIDUA model in this context and offer practical insights for robot design and implementation.

From Innovation To Impact: Evaluation Of The Shape Accelerator Cohort, Iryna V. Lendel, Chloe Wieber

Upjohn Institute Technical Reports

The Solutions Hub for an Alternative Packaging Ecosystem (SHAPE), a Michigan-Ohio partnership operated by the Michigan State University Research Foundation as part of a National Science Foundation Regional Innovation Engine, co-funded a special cohort of the NextCycle Accelerator focused on sustainable packaging and circular economy ventures. This report evaluates how the accelerator contributed to firm-level advancement and ecosystem-level change, drawing on program data and interviews with participating companies, program facilitators, stakeholders, and SHAPE management.  Participants most consistently credited coaching, network connections, and the Pitch Showcase with meaningfully impacting their business trajectory, gaining market traction, advancing technology readiness, and gaining access …

Chacahoula 2026, Volume 93, Pawan Khatri, Alayna Pellegrin

Chacahoula

The 2026 issue of Chacahoula, There's No Place Like Home, emphasizes the way in which our university becomes a home to students.  Featuring prize-winning poetry from Allyson Gee, Nick Fredrick, Amaris Kendall and Anmol Subedi, the book includes December 2025 and May 2026 graduating classes, as well as stunning photography of  campus life on the banks of Bayou Desiard. With features covering students, faculty, and staff, this year's time capsule preserves an academic year in the life of the University of Louisiana at Monroe. This edition includes a video message from President Carrie Castille. Download the PDF to your device …

Uae Audiences' Reliance On Social Media As A Source Of Information On Complementary And Alternative Medicine: A Study Of Al Ain City Residents Based On Media System Dependency Theory, Abdul Rahman Al-Toum, Mohammed Al-Kaabi, Najoud Al-Mansouri, Ahmed Bin Ishaq, Amer Khaled Ahmad, Maram Manajrah

This study examined the extent to which UAE audiences rely on social media as a source of information on complementary and alternative medicine (CAM) and the resulting effects of this reliance. Grounded in Media System Dependency Theory, the study employed an audience survey using a questionnaire administered to a sample of (132) Emirati citizens in Al Ain. The findings revealed that the combined medium and high reliance reached (53%). Regarding the effects, cognitive effects recorded the highest arithmetic means, followed by affective and behavioral effects at comparable levels. Hypothesis testing showed no statistically significant differences in the degree of reliance …

When Does Global Search Pay For Itself? A Measured Exploration Cost And Energy Break-Even Analysis Of A Metaheuristic Search Controller In Solar Energy Systems, Mohamed Ali Muammar Ezgour

Communications of the IIMA

Autonomous energy systems increasingly delegate the choice of operating point to embedded search algorithms, trading a fast local optimizer that can settle on a wrong point against a slower global search that guarantees the right one at a measurable cost. This paper reframes maximum power point tracking under partial shading as that decision and measures its economics on a fixed photovoltaic plant in MATLAB/Simulink. A Hippopotamus Optimization global search handed over to incremental conductance is compared with incremental conductance alone across seventeen initial duty cycles and thirty random seeds. The hybrid reached the global peak in all thirty seeds, whereas …

Bitseat: Reimagining The Financing For Airliners Using Nonfungible Tokens (Blockchain Technology), Edwin S. Ongola

Journal of Aviation Technology and Engineering

This essay describes how blockchain technology, particularly nonfungible tokens, can be used to raise funding for airliners. The essay begins with a brief overview on the costs, categories, and acquisition methods of airliners. After that, the essay introduces concepts on blockchain technology, tokens, and smart contracts. The essay then touches on how nonfungible tokens can be used to facilitate fractional ownership of airliners. From there, the essay discusses Bitseat, a conceptual nonfungible token for fractional ownership of airliners, covering its overall design, appeal, marketplace alternatives, and challenges. Finally, in the discussion, the essay summarizes the overall concept and outlines its …

Developing A Deep Learning-Based Artificial Intelligence System For Detecting Scientific Misinformation On Digital Platforms, Shorouq Al-Awawdeh, Ayah Al- Jafari

Objectives: This study develops and evaluates an Arabic scientific misinformation detection system by fine-tuning AraBERT-base-v2. It examines the effects of early stopping and input sequence length on model performance, interprets selected linguistic characteristics associated with misleading content, and discusses the limitations of using machine-translated data.

Methodology: The study adopted a mixed-methods design, employing a systematic integration of quantitative and qualitative approaches, supported by an interpretive qualitative reading. The initial database consisted of 23,546 records, including 123 Arabic articles collected from the Akeed, Sheek, and Taqeen platforms, and 23,423 foreign-language records drawn from the GossipCop and PolitiFact collections within FakeNewsNet. After …

From Data To Decision-Making: The Role Of Local Digital Twins In Cross-Domain Management Within Municipalities – A Research-In-Progress Study In Veenendaal, Diana M.E. Boekman, Koen Smit, Guido Ongena, Rob Peters

Communications of the IIMA

Municipalities are facing increasingly complex, interconnected challenges in areas like housing, climate adaptation, mobility, and social policy. Local Digital Twins (LDTs) are seen as a promising tool to make this complexity more understandable and support decision-making. At the same time, both literature and practice show that few initiatives get past the pilot phase, even though getting through that phase is essential for successful long-term adoption.

This paper presents a research-in-progress study on the development and application of an implementation method for LDT technology within the municipality of Veenendaal, based on human values rather than driven by technological possibilities. Based on …

A Novel Entropy Based Maintainability Measurement Algorithm For Java Source Code., Remi M. Yusuf Mr, Md Shadab Mashuk, Julian Bass

Communications of the IIMA

Software metrics play a central role in assessing and managing the quality of software systems providing quantitative insights into attributes such as complexity, reliability, rigidity, modifiability and maintainability. Among these, maintainability is particularly critical, as it directly influences the ease of system evolution, long-term sustainability, and overall cost effectiveness. Despite the widespread use of metric-based maintainability measurement algorithms, capturing a value that reflects the maintainability situation of software source code remains a challenging task, especially in the presence of design deficiencies such as code smells. To measure changes in maintainability, this study experimentaly characterises the relationship between code smells and …

From Dissertation To Deployment: A Unified Software Platform Operationalizing Clinical-Prediction And Sequential-Security Ai For Healthcare, Olsi Shehu, Damiana Teliti, Jasmin Kevrić, Bekir Karlik

Communications of the IIMA

Advances in machine learning for healthcare are abundant, yet most validated models remain confined to research notebooks and never reach secure, usable clinical software. This paper addresses that deployment gap by presenting a unified, security-hardened software platform that operationalizes two complementary streams of doctoral research inside a single, role-based hospital information system. The first stream contributes a clinical-prediction capability: an ultra-hybrid ensemble that couples a quantum-inspired feature transformation, particle-swarm feature selection, and calibrated soft voting for cancer-outcome prediction (96.41% accuracy, AUC-ROC 0.983 on TCGA-BRCA), survival stratification, multi-cancer generalization, and pharmacogenomic drug-response classification (89.31% mean accuracy across 25 compounds). The second …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …